Prediction-Based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers
Bibliographic record
Abstract
In a cloud computing environment, users using the pay-as-you-go billing model can relinquish their services at any point in time and pay accordingly. From the perspective of the Cloud Service Providers (CSPs), this is not beneficial as they may lose the opportunity to earn from the relinquished resources. Therefore, this paper tackles the resource assignment problem while considering users relinquishment and its impact on the net profit of CSPs. As a solution, we first compare different ways to predict user behavior (i.e. how likely a user will leave the system before its scheduled end time) and deduce a better prediction technique based on linear regression. Then, based on the RACE (Relinquishment-Aware Cloud Economics) model proposed in [1], we develop a relinquishment-aware resource optimization model to estimate the amount of resources to assign on the basis of predicted user behavior. Simulations performed with CloudSim show that cloud service providers can gain more by estimating the amount of resources using better prediction techniques rather than blindly assigning resources to users. They also show that the proposed prediction-based resource assignment scheme typically generates more profit for a lower or similar utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".